Internet of Things Applications in Production Systems
A. Boza, B. Cortés, L. Cuenca and F. Alarcón
Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València,
València, Spain
Keywords: Production System, Internet of Things (IoT), RFID, Information Systems.
Abstract: The Internet of Things field has been applied in industries for different purposes. This paper presents a
literature review of Internet of Things applications in the production system. A taxonomy with five
categories has been employed in this review: Sector, Technology, Production Phase, Practical Application
and Benefit. The sectors, technology and production phase where IoT is being introduced practically or
theoretically have been identified, and the benefits of IoT in production systems have been collected and
classified. This research presents the advantages of applying Internet of Things in production systems,
which helps not only production systems managers in practical implementations, but also researchers to
identify research gaps for future research.
1 INTRODUCTION
Although no universal definition exists for Internet
of Things (IoT), the core concept is that everyday
objects can be equipped with identifying, sensing,
networking and processing capabilities, which will
allow them to communicate with one another, and
with other devices and services, over the Internet to
achieve some useful objective (Atzori et al., 2010).
According to Miorandi et al., (2012), the three main
system-level characteristics of IoT are: Anything
communicates, Anything is identified and Anything
interacts.
New technologies are necessary to apply IoT.
Gubbi et al., (2013) identify three more technical
components of IoT: (a) Hardware, made up of
sensors, actuators and embedded communication
hardware; (b) Middleware, in demand storage and
computing tools for data analytics; and (c)
Presentation, novel easy-to-understand visualisation
and interpretation tools which can be widely
accessed on different platforms, and can be designed
for distinct applications.
Based on a review of the literature, Whitmore et
al., (2014) classify IoT applications into the
following domains: smart infrastructure, healthcare,
supply chains/ logistics, and social applications. In
the supply chain/logistics domain, IoT can further
improve efficiency in all the supply chain parts:
production, distribution, transporting, and etc. The
literature includes a few research works about IoT
applied to production. This paper reviews them for
the purpose of acquiring better knowledge about IoT
applied to production, and also about the benefits of
these proposals. We expect to show how the
application of IoT can help managers of the
production system in enterprises. For this review,
Section 2 shows the methodology employed, Section
3 explains the taxonomy used to classify the
different research works, and Section 4 includes the
conclusions drawn from this research.
2 REVIEW METHODOLOGY
The literature on IoT applied to production was
searched in scientific-technical bibliographic
databases Google Academics and Scopus, which
include publishing portals like Elsevier, IEEExplore
or Springer. The literature on IoT applied to
production was searched in scientific-technical
bibliographic databases Google Academics and
Scopus, which include publishing portals like
Elsevier, IEEExplore or Springer.
The search criteria applied were combinations of
“production”, “production system”, “manufacturing”
with “Internet of Things”, ”Internet of
Manufacturing Things” and “IoT” in the titles and
keywords of the papers. In the first search, 31
references were found in the last 4 years (2010-
330
Boza A., Cortés B., Cuenca L. and Alarcón F..
Internet of Things Applications in Production Systems.
DOI: 10.5220/0005378903300337
In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 330-337
ISBN: 978-989-758-096-3
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
2014). These papers were reviewed and 21
references, which actually dealt with the proposal of
this paper, were selected. These references were
obtained from journals (23,81%), book sections
(23,81%) and conferences (52,38%). Table 1 shows
the papers found, the difficulties presented in each
paper and the proposal to solve them with IoT.
3 TAXONOMY
The taxonomy employed in this review considers
five categories. Some of these categories have been
proposed after considering that they have been
helpful in other production and supply chain reviews
like (Mula et al., 2010), (Carnevalli and Miguel,
2008) or (Melo et al., 2009). These categories are:
Sector: this represents the industrial sector where
IoT has been applied; e.g., garments, agriculture, etc.
Each sector has its own production processes.
Technology: this presents the technologies that have
been used to apply IoT.
Production Phase: all the selected references focus
on one part of the production system given the
complexity of this system. This category indicates
the production system phase that the papers have
addressed.
Table 1: Literature reviewed.
Author Problem
Cao et al., (2011) A new architecture of the toy production material tracking system based on key technologies of IoT
Castro et al., (2011)
A management application architecture based on IoT for the identification and classification of
automatic oxygen cylinders to make all the information available during the production process
Cuiyun and Yuanhang (2010) A study of the influence of an IoT application on production and logistics in an enterprise
Houyou et al., (2012)
Automation systems in manufacturing supported by IoT to support flexibility and agility in
manufacturing
Hu et al., (2011)
An IoT monitoring platform to monitor all the critical control points (CCPs) to ensure food safety
during a sausage production process
Isenberg et al., (2011)
Research about suitability and cooperation in collaborative production environments for autonomic and
agile processes based on IoT and autonomous objects
Lee et al., (2012)
The use of radio frequency identification (RFID) technology to capture real-time data, which is helpful
in monitoring resources utilisation during production.
Liu and Xu (2013) An integrated management framework of a satellite product manufacturing workshop based on IoT
Lvqing (2011)
A mechanical production monitoring system based on IoT technology to replace traditional manual
entry method
Meyer et al., (2011)
An approach for a monitoring and control system to enable new ways in which disturbances can be dealt
with in order to increase the robustness of overall plan execution
Qu et al., (2012) A traceability system based on IoT to address the food safety problems of cucumbers
Stephan et al., (2010)
Using Digital Object Memories throughout a product’s life cycle by focusing on the production part of
the value chain
Vossiek et al., (2010)
Skilful combination of different Auto-ID technologies and the incorporation of sensor data provided by
production machinery and control systems
Wang and Chen (2013) A new manufacturing inventory management model based on IoT technology
Wang and Liu (2014)
Applications of IoT technology to the agricultural products supply chain to improve the operation
efficiency of a supply chain of agricultural products
Wuest et al., (2012)
A Product Avatar representation of product lifecycle information of an Intelligent Product on the social
network Facebook
Yuan et al., (2013)
Development of a system based on IoT technology to verify that IoT promotes workshop process
visualisation developments
Zhang et al., (2014)
The proposed IoMT aims to design an easy-to-deployment infrastructure to form an active sensing
manufacturing environment and to timely monitor, control and optimise the production process.
Zhiliang et al., (2013)
A new project that merges Personal Digital Assistant (PDA) in manufacturing shop with Workshop
Internet of Things (WIoT).
Zuehlke (2010)
Smart Factory KL, a multi-vendor research and demonstrator facility for smart production technologies
based on IoT
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Table 2: IoT in production systems: Industrial Sectors.
Sector Author
General
Cuiyun and Yuanhang (2010), Isenberg et al.,
(2011), Houyou et al., (2012), Lvqing (2011),
Meyer et al., (2011), Wang and Chen (2013),
Stephan et al., (2010), Wuest et al., (2012),
Yuan et al., (2013), Zhang et al., (2014),
Zhiliang et al., (2013), Zuehlke (2010)
Agriculture
Qu et al., (2012), Shengduo and Jian (2012),
Wang and Liu, (2014)
Food Hu et al., (2011)
Toys Cao et al., (2011)
Garment Lee et al., (2012)
Metal Vossiek et al., (2010)
Aerospace Liu and Xu (2013)
Chemical Castro et al., (2011)
Practical Application: many research works provide
a proposal as to how to apply IoT in production, but
they do not apply the proposal to a real company. So
this approach indicates if the proposal has been
validated in a real situation or not.
Benefit: this category includes the benefits from
applying IoT in the production system in accordance
with the reviewed literature.
3.1 Sector
Basing on the industrial sector, papers can be
classified into a) a general sector (if the paper
presents an application in production, but does not
specify a sector); or b) a specific sector, where seven
industrial sectors were identified: Food, Toys,
Garments, Agriculture, Metal, Aerospace and
Chemical. Table 2 shows the sectors found and the
authors who applied his approach in each sector.
3.2 Technology
According to (Tan and Koo, 2014), there are three
kinds of technologies in IoT for data acquisition:
two-dimensional code, RFID and sensors. Two-
dimensional (2D) codes use black and white pixels
laid out on a plane to store information. RFID is a
wireless automatic identification technique that uses
radio frequency signals to identify a target and to
obtain messages. Sensors are objects that can
acquire data. This classification has been used as a
basis to review the technologies included in the
reviewed literature. Table 3 provides the data
acquisition technologies to implement IoT.
3.3 Production Phase
According to (Cuatrecasas, 1994), the production
Table 3: IoT in the production system: Technology.
Author 2D codes RFID Sensors Others
Cao et al., (2011) X X X Servers
Castro et al., (2011) X X 6LoWPAN
Cuiyun and Yuanhang
(2010)
X
Server
Houyou et al., (2012) X Wireless network
Hu et al., (2011) X X Wireless network
Isenberg et al., (2011) X X
Lee et al., (2012) X X X Mobile Communication Network, Cameras
Liu and Xu (2013) X X Server, Wireless Network, GPRS
Lvqing (2011) X X X
Meyer et al., (2011) X X X Three dimensional code, GPS, Wireless network, Server
Qu et al., (2012) X 3G
Shengduo and Jian (2012) X X X Video Surveillance Technology, GPS
Stephan et al., (2010) X X Object Memory Server
Vossiek et al., (2010) X X
Wang and Chen (2013) X X
Wang and Liu (2014) X X Wireless and wired network, Server
Wuest et al., (2012) Social Network
Yuan et al., (2013) X X Wireless Network
Zhang et al., (2014) X X Zigbee
Zhiliang et al., (2013) X X Wireless network, LAN, Server
Zuehlke (2010) X X Bluetooth, Zigbee, Wireless Network
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system is composed of three phases: Planning
Operation and Control. Planning is composed of
Sales Forecasting, Capacity Planning, Production
Planning and Production Order and Programming.
The Operation phase includes Replenishment and
Material and Resources Management, Productive
Process and Client Supply. In the control phase, the
company must be sure that product requirements
have been achieved, which represent Production,
Quality and Stock Control.
Table 4: Subphase codes.
Phase Subphase Code
Planning
Sales Forecasting SF
Capacity Planning CP
Production Planning PP
Production Order and
Programming
PO&P
Operations
Replenishment and Material
and Resources Management
R&MRM
Productive Process PPS
Client Supply CS
Control
Production PC
Quality QC
Stock SC
These phases have been used to classify the
reviewed based on the production phase that the
literature authors have dealt with. Table 4 provides
the code of these phases and Table 5 classifies each
paper according to the production phase (or phases)
where IoT has been applied.
3.4 Practical Application
Five references include real IoT applications for the
production area. Lee et al., (2012) apply their
proposal in THC, a Hong Kong-based garment
manufacturing company. Zhang et al., (2014)
presents an industrial case study by applying their
proposed event model to a shop floor to analyse its
key production performance. Isenberg et al., (2011)
design a scenario that illustrates an autonomous
assembly system for an automotive tail-light by
working with IoT. (Vossiek et al., 2010) do not
apply their proposal in any company, but their
research is based on real company information.
Finally, (Castro et al., 2011) carry out a proposal
with collaboration from the operators and/or staff
responsible for managing oxygen cylinders.
The benefits of these real applications have been
included in the following point.
Table 5: Production phases of the reviewed works.
Author
Planning Operations Control
SF CP PP PO&P R&MRM PPS CS PC QC SC
Cao et al., (2011) O O
Castro et al., (2011) O O O
Cuiyun and Yuanhang (2010) X X X
Houyou et al., (2012) X X
Hu et al., (2011) O O
Isenberg et al., (2011) X X
Lee et al. (2012) O X X
Liu and Xu (2013) X
Lvqing (2011) X
Meyer et al., (2011) X X O
Qu et al., (2012) O O
Shengduo and Jian (2012) O O
Stephan et al., (2010) O O
Vossiek et al., (2010) O O
Wang and Chen (2013) X O
Wang and Liu (2014) O
Wuest et al., (2012) O X O O
Yuan et al., (2013) O
Zhang et al., (2014) X X O
Zhiliang et al., (2013) O O
Zuehlke (2010) X X
Total 0 2 1 3 4 9 15 7 3
*X represents the phase of the work indicated by the author.
O represents the phase of the work deduced by the content of the paper.
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333
3.5 Benefits
Cuiyun and Yuanhang (2010) identify benefits for
the production area into two main groups: 1)
benefits related to information management in
production, 2) Other benefits in production.
According to this identification, all the benefits
found in this review are classified into the following
groups.
In the first group, the benefits found are: unique
identifier, each product has its own identifier in the
production system, without repetition; monitoring
reading information, information is taken
automatically and can be supervised; real-time
information, managers can see all the information on
the real status in production; more accessible
information, managers can take production
information and sharing in the easiest way;
improving information quality, monitoring
information is more accurate and free of errors; and
improving transparency information, it is possible to
obtain further information, which was previously
hidden.
Table 6: Benefits for production systems.
Benefits Acronym
Information
Unique identifier UI
Monitoring reading information MRI
Real-time information RTI
More accessible information MAI
Improving information quality IIQ
Improving transparency of
information
ITI
Other
Reducing production cost RPC
Improving quality production IQP
Improving efficiency IE
Improving service level ISL
Better synchronisation of processes BSP
Reducing overstocking and
understocking
ROU
Monitoring production MP
Better quality product BQP
Other benefits covers reducing production costs,
information is of better quality and the waste
produced by mistakes in production can be avoided;
improving quality production, like detecting real-
time disturbances; improving efficiency, like
reducing energy consumption; improving service
level, with quicker processes and better quality
information; better synchronisation of processes,
due to the real-time information provided; reducing
overstocking and understocking, transparency-
related information avoids possible mistakes in
production and allows better control in production;
monitoring production, due to the intelligence that
IoT provides the system with to better control
production.
To summarise these benefits, Table 6 provides
acronyms of each benefit. Table 7 identifies the
papers that deal with these benefits and the
percentages of each benefit are represented in the
graphs in Figures 1 and 2. These graphs show that
the most repeated benefit in the first group
(information management) is real-time information
and the most repeated benefit in the second group
(other benefits) is improving quality production.
Figure 1: Percentages of benefits in information.
Figure 2: Percentages of other benefits.
Table 8 identifies the benefits in each production
phase. The cells inside the table show the number of
papers that include the benefit in that phase. The
bottom of the table summarises the number of
benefits cited in that production phase. Very few
papers deal with IoT in the planning and operation
production phase, so reported benefits are limited.
However, it is important to highlight the capacity
planning phase since only two papers deal with this
phase, although ten benefits were identified in these
papers for this phase. The phase with the most
benefits is Client Supply. The benefit that appears
the most is Real-Time Information, which appears in
eight different phases.
4 CONCLUSIONS
This work reviews IoT applications in production
systems. To do this review, a taxonomy based on the
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Table 7: Benefits of the reviewed works.
Authors
Benefits in information Other benefits
UI
MRI
RTI
MAI
IIQ
ITI
RPC
IQP
IE
ISL
BSP
ROU
MP
BQP
Cao et al., (2011) X
Castro et al., (2011) X X X
Cuiyun and Y. (2010) X X X X X X X X X X
Houyou et al., (2012) X X X X
Hu et al., (2011) X X X X X X
Isenberg et al., (2011) X
Lee et al., (2012) X
Liu and Xu (2013) X X X
Lvqing (2011) X
Meyer et al., (2011) X X X X X X X
Qu et al., (2012) X X X
Shengduo and J. (2012) X X X X X
Stephan et al., (2010) X X X X X X X X
Vossiek et al., (2010) X X X X X
Wang and Chen (2013) X X X
Wang and Liu (2014) X X
Wuest et al., (2012) X X
Yuan et al., (2013) X X X X
Zhang et al., (2014) X X
Zhiliang et al., (2013) X X X X X X X
Zuehlke (2010) X X X X X
Total Percentage (%) 11 13 37 22 6 11 19 24 16 3 11 3 19 5
Table 8: Benefits in each production phase of the reviewed works.
SF CP PP PO&P R&MRM PPS CS PC QC SC
UI 0100 0 1252 1
MRI 0 1 0 0 0 1 2 6 2 1
RTI 0 2 1 0 2 3 7 12 5 2
MAI 0 1 0 0 2 1 5 8 3 2
IIQ 0100 0 0222 1
ITI 0 0 0 1 2 0 2 3 3 1
RPC 0 1 0 0 1 3 3 5 2 0
IQP 0 1 0 0 1 2 3 7 4 1
IE 0100 1 1251 0
ISL 0 1 0 0 0 0 1 1 0 0
BSP 0 0 0 0 1 1 1 3 2 1
ROU 0 0 0 0 1 0 1 4 0 0
MP 0001 0 3122 1
BQP 0 1 0 0 0 0 2 2 1 1
Benefits
cited
0 10 1 2 8 9 14 14 12 10
* Cells show the number of papers that include the benefit in that phase
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335
analysis of five characteristics was used: Sector,
Technology, Practical Application, Production Phase
and Benefit.
By way of conclusion, we can state that IoT is a
new research field in production systems. It is
possible to find general proposals for a wide range
of industrial sectors, but also a specific proposal for
given industrial sectors. However, very few of these
proposals have had a real application in industry.
The predominant technologies in the application
of IoT for production systems are RFID and sensors.
Regarding the planning, operation and control
phases in production systems, the application of IoT
focuses mainly on the control phase.
Most of the found proposals are applications to
production control, followed by quality control and
stock control. All the authors indicate that IoT
applications contribute with benefits in production
systems. These benefits are not only for better
management information in production (e.g., real-
time information or improving information quality),
but also benefits for other aspects of the production
system (e.g., reducing production costs and
improving efficiency).
In this review, some gaps in the literature on IoT
application in production system have been
identified. Based on these gaps and seeing how IoT
applications can help managers of the production
system, some near future lines of research arise: 1)
most proposals are general for the production of any
company, but there are very few applied proposals;
2) the predominant technology used to implement
IoT is RFID. Although it offers many advantages,
IoT with other technologies would be interesting for
it to be applied or combined with it; 3) some
industrial sectors, where experiments with IoT are
being done, have been identified, but new research
works could be conducted in other industrial sectors;
4) IoT Applications in the production area
concentrate mainly on the control phase. Hence
further research should also be conducted in
planning and operation phases and subphases.
ACKNOWLEDGEMENT
This research has been carried out in the framework
of the project PAID-06-21 Universitat Politècnica de
València and GV/2014/010 Generalitat Valenciana
(Emergent Research Groups)
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